Method for evaluating driving ability of intelligent networked truck platoon leader driver in typical scenario

Abstract

The application relates to a kind of intelligent network connection truck platoon lead vehicle driver driving ability evaluation methods in typical scene, comprising: obtaining the running state data of truck platoon in typical scene;Construction platoon running characteristic index system, determine the operation behavior threshold of lead vehicle driver based on the best running state in the running state data;Based on the operation behavior threshold, carry out desktop video experiment, obtain experimental raw data;Based on the experimental raw data, build the relationship model between driver driving ability and operation behavior;Based on the operation behavior threshold, the relationship model is solved reversely, and the quantitative evaluation result of lead vehicle driver driving ability is obtained.The application provides a set of systematic and quantifiable evaluation method for intelligent network connection truck platoon lead vehicle driver driving ability evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent connected platooning, and in particular to a method for evaluating the driving ability of a driver of a lead vehicle in an intelligent connected truck platoon under typical scenarios. Background Art

[0002] By combining two or more autonomous trucks, intelligent connected truck platooning can reduce headway time during driving, significantly improving traffic capacity, helping to alleviate traffic congestion and improve logistics transportation efficiency. However, under the current technical architecture of autonomous driving systems, autonomous vehicles may still face certain situations that they cannot handle automatically. When the vehicle encounters a situation that exceeds the design domain of the autonomous driving system, the driver must manually intervene or take over. Therefore, truck platooning and cooperation with a pilot vehicle driver will become a new operating model. This model requires the professional skills and experience of the pilot vehicle driver to compensate for the shortcomings of the autonomous driving platooning system in certain situations, ensuring that the truck platoon can continue to operate safely and stably in complex road conditions, inclement weather, or other unpredictable situations. Therefore, the driving ability of the pilot vehicle driver has a significant impact on the operating characteristics of intelligent connected truck platooning and is one of the key factors in ensuring the safe and smooth operation of the platoon.

[0003] Current research on driver performance focuses primarily on the perception, attention, reaction speed, maneuvering skills, fatigue, and typical driving behavior of traditional vehicle drivers, as well as the synergistic interaction between driver performance and autonomous driving systems in connected single-vehicle scenarios. Due to the recent emergence of intelligent connected platooning technology, research on the driving performance of pilot vehicle drivers is relatively scarce, with most research focusing on truck platooning technology itself. Unlike traditional truck drivers and autonomous truck safety officers, the pilot vehicle driver in a truck platoon not only bears the responsibility of monitoring the real-time operating status of their own vehicle, quickly taking over and controlling the vehicle when encountering dangerous situations, special events, inclement weather, and other situations beyond the design domain of the autonomous vehicle, but also constantly monitors the following status and requests of other trucks in the platoon to maintain optimal platooning operation. However, the standard of pilot vehicle driver performance required to achieve optimal truck platooning operation remains an urgent issue. Summary of the Invention

[0004] The purpose of the present invention is to propose a method for evaluating the driving ability of the driver of a pilot vehicle in an intelligent connected truck platoon under typical scenarios, so as to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for evaluating the driving ability of a pilot vehicle in a platoon of intelligent connected trucks in a typical scenario includes:

[0007] Obtain operational status data of truck platoons in typical scenarios;

[0008] Constructing a formation operation characteristic index system and determining a control behavior threshold of the lead vehicle driver based on the best operation state in the operation state data;

[0009] Based on the manipulation behavior threshold, a desktop video experiment is conducted to obtain raw experimental data;

[0010] Based on the raw data of the experiment, a relationship model between the driver's driving ability and control behavior is constructed;

[0011] The relationship model is reversely solved based on the control behavior threshold to obtain a quantitative evaluation result of the driving ability of the pilot vehicle driver.

[0012] Optionally, obtaining the operational status data of a truck platoon in a typical scenario includes:

[0013] Determine the pilot car driver's control behavior and the characterization indicators of control behavior in typical scenarios;

[0014] Based on the control behavior and characterization indicators, truck platoon simulation is carried out to obtain the operating status data of the truck platoon.

[0015] Optionally, the operating status data of the truck formation includes: basic information of the truck formation, location information, operating data and energy consumption and emission data;

[0016] The basic information of the truck platoon includes: simulation time and vehicle number;

[0017] The position information includes: horizontal position coordinates and vertical position coordinates;

[0018] The operating data includes: longitudinal speed, lateral speed, longitudinal acceleration, lateral acceleration, steering wheel angle, collision time, collision time countdown, vehicle distance, and headway;

[0019] The energy consumption and emission data include: CO2 emissions, NO x Emissions, CO emissions, HC emissions, and fuel consumption.

[0020] Optionally, the formation operation characteristic index system includes: stability dimension, safety dimension and ecological dimension;

[0021] The stability dimensions include: longitudinal speed, longitudinal acceleration, and vehicle-to-vehicle distance;

[0022] The longitudinal velocity is:

[0023]

[0024] Where V represents the longitudinal velocity, V i represents the longitudinal velocity of the i-th vehicle in the truck platoon, and n represents the number of vehicles in the platoon;

[0025] The longitudinal acceleration is:

[0026]

[0027] Where A represents the longitudinal acceleration, A i represents the longitudinal acceleration of the i-th vehicle in the truck platoon;

[0028] The workshop distance is:

[0029] g i =x i+1 -x i -l

[0030] Among them, g i represents the distance between the i-th vehicle and the vehicle in front, x i represents the position of the i-th vehicle; l represents the length of the vehicle in front of the i-th vehicle;

[0031] The safety dimensions include: collision time countdown, headway, and lane change area;

[0032] The inverse of the collision time is:

[0033] ITTC=(v f -v r ) / (Sl)

[0034] Among them, v f Indicates the speed of the preceding vehicle, v r It represents the speed of the rear vehicle, S represents the distance between the front and rear vehicles, and ITTC represents the inverse time to collision.

[0035] The headway time is:

[0036]

[0037] Among them, THW represents time headway;

[0038] The lane-changing area is the space required for the platoon vehicles to change lanes on the road.

[0039] The ecological dimensions include: fuel consumption saving rate, CO emission saving rate, CO2 emission saving rate, NO x Emission savings rate.

[0040] Optionally, the desktop video experiment includes: pre-experiment subjective testing, pre-experiment driving training, pre-experiment and formal experiment;

[0041] The subjective test before the experiment was: a driving ability test based on a subjective questionnaire;

[0042] The formal experiment is a manipulation behavior test based on a desktop video experiment.

[0043] Optionally, the driver's driving ability includes: theoretical knowledge of truck platooning, practical skills of truck platooning and psychological state of driver's life.

[0044] Optionally, the relationship model between the driver's driving ability and control behavior is:

[0045] y1=a o +a1x1+a2x2+a3x3+ε1

[0046] y2=b o +b1x1+b2x2+b3x3+ε2

[0047] Among them, y1 represents the prediction model between driving ability and takeover behavior, y2 represents the prediction model between driving ability and lane-changing behavior, x1 represents the theoretical knowledge score, x2 represents the practical skill score, x3 represents the psychological state score, a1, a2, a3, b1, b2, b3 represent the coefficients in the constraint conditions, a0, b0 are the intercept terms in the linear regression model, which represent the expected values ​​of the dependent variables (y1, y2) when the values ​​of all independent variables (x1, x2, x3) are zero. ε1 ,ε2 is the residual, which represents the random fluctuations that the model cannot explain or the random errors in the data.

[0048] Optionally, reversely solving the relationship model based on the manipulation behavior threshold includes:

[0049] Taking the driver's control behavior level as the optimization target and the control behavior threshold as the constraint condition, a solution model for the minimum driving ability requirement is constructed;

[0050] The relationship model is solved based on the solution model.

[0051] Optionally, the solution model is:

[0052] Objective function: Min Z = c1x1 + c2x2 + c3x3

[0053] Constraints:

[0054] a0+a1x1+a2x2+a3x3+ε1≥y1

[0055] b0+b1x1+b2x2+b3x3+ε2≥y2

[0056] Capacity constraints:

[0057] x1+x2+x3≤300

[0058] Non-negativity constraints:

[0059] x1,x2,x3≥0

[0060] in, Z Indicates the control time. S.t Indicates constraints.

[0061] The beneficial effects of the present invention are:

[0062] The present invention provides a systematic and quantifiable evaluation method for evaluating the driving ability of the driver of the pilot vehicle in an intelligent connected truck platoon.

[0063] The data collection method of the present invention is accurate and effective. It uses micro-traffic simulation technology to restore the truck platooning test scenario and obtain data on the entire process of the platoon's operation status. The platoon operation characteristic index system takes into account the three levels of stability, safety, and ecology. From a macro perspective, subjective questionnaires and desktop experiments are used to obtain driving ability and control behavior data respectively.

[0064] The driving ability evaluation method of the intelligent connected truck platoon leader vehicle in the typical scenario of the present invention is generally applicable to other scenarios.

[0065] The present invention can effectively reduce the safety risks and experimental costs during the testing process through traffic simulation technology and desktop experiments.

[0066] The driving ability evaluation method of the truck platoon pilot vehicle driver of the present invention can provide a theoretical basis for the selection, training and assessment of truck platoon pilot vehicle drivers, and facilitate the implementation of intelligent connected truck platooning technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0068] Figure 1 Schematic diagram of the operating characteristic index system of intelligent connected truck platooning according to an embodiment of the present invention;

[0069] Figure 2 The figure is a flow chart of a method for evaluating the driving ability of a pilot vehicle in an intelligent connected truck platoon under a typical scenario of an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] This embodiment, based on the clarification of the driving ability requirements of the truck platoon leader vehicle driver, collects data through micro-traffic simulation technology and desktop experiments, determines the control behavior level threshold based on the platoon operation characteristics, and quantitatively evaluates the driving ability performance of the leader vehicle driver in typical scenarios, providing a theoretical basis for the selection, training and assessment of truck platoon leader vehicle drivers.

[0073] This embodiment proposes a method for evaluating the driving ability of a pilot vehicle driver in a typical scenario of an intelligent connected truck platoon, including:

[0074] Obtain operational status data of truck platoons in typical scenarios;

[0075] Build a formation operation characteristic index system and determine the control behavior threshold of the pilot vehicle driver based on the optimal operation state in the operation state data;

[0076] Based on the manipulation behavior threshold, a desktop video experiment was conducted to obtain the original experimental data;

[0077] Based on the original experimental data, a relationship model between the driver's driving ability and control behavior is constructed;

[0078] The quantitative evaluation results of the pilot vehicle driver's driving ability are obtained by reversely solving the relationship model based on the control behavior threshold.

[0079] Furthermore, obtaining the operational status data of the truck platoon in a typical scenario includes:

[0080] Determine the pilot car driver's control behavior and the characterization indicators of control behavior in typical scenarios;

[0081] Based on the control behavior and characterization indicators, truck platoon simulation is carried out to obtain the operating status data of the truck platoon.

[0082] Specifically, in this embodiment, determining the pilot vehicle driver's control behavior and the characterization index of the control behavior in a typical scenario includes:

[0083] The typical scene of intelligent networked truck platoon is selected to determine the driving behavior of the lead vehicle driver, as well as the representation index and parameters thereof; specifically including:

[0084] The small debris scattered on the expressway is selected as the typical scene, and the takeover behavior and lane changing behavior are selected as the typical driving behavior. Among them, the takeover reaction time is taken as the representation index of the takeover behavior, which is defined as the duration from the vehicle sending a takeover request or the driver discovering the obstacle to the driver taking over the vehicle control, and the horizontal range is 1-5s; the lane changing duration is taken as the representation index of the lane changing behavior of the lead vehicle driver, which is defined as the time interval from the driver starting to control the vehicle to the vehicle completing the lane changing operation, and the horizontal range value is 3-13s.

[0085] Based on Plexe-SUMO, a simulation platform is built to simulate truck platoon and obtain truck platoon running state data; specifically including:

[0086] Taking the Beijing section of Beijing-Xiong'an Expressway as an example, a 10km one-way four-lane expressway basic section is built as the simulation road scene according to the actual road geometry and other information using SUMO simulation software. Then, a typical model of 3 truck platoons is built by combining PLEXE third-party platoon creation tool, and the road, traffic and environmental conditions of the test scene are restored. The running parameters of the platoon vehicles are determined by referring to the previous research results, and different control level parameters are input by programming to control the platoon behavior, and the platoon running state data of the entire driving process is obtained synchronously.

[0087] Specifically, in the present embodiment, the running state data of the truck platoon includes: truck platoon basic information, position information, running data and energy consumption emission data;

[0088] The truck platoon basic information includes: simulation time and vehicle number;

[0089] The position information includes: lateral position coordinates and longitudinal position coordinates;

[0090] The running data includes: longitudinal speed, lateral speed, longitudinal acceleration, lateral acceleration, steering wheel angle, collision time, collision time reciprocal, inter-vehicle distance, and headway;

[0091] The energy consumption emission data includes: CO2 emission, NOx emission, CO emission, HC emission, and fuel consumption. x

[0092] An index system of platoon running characteristics is built, and the driving behavior threshold of the lead vehicle driver is determined based on the best running state in the running state data;

[0093] Further, the index system of truck platoon running characteristics can be divided into three dimensions of stability, safety and ecology.​

[0094] The calculation formulas for the secondary indicators and quantitative data in the stability dimension are as follows:

[0095] (1) Longitudinal speed: The speed of the truck platoon vehicles moving in the longitudinal direction, reflecting the relative movement and mutual influence of the vehicles in the platoon in the longitudinal direction. The calculation formula is as follows:

[0096]

[0097] Where V i is the longitudinal velocity of the i-th vehicle in the truck platoon; 1<i≤n, n is the number of vehicles in the platoon.

[0098] (2) Longitudinal acceleration: The rate of change of the velocity of the vehicles in the formation in the longitudinal direction, which reflects the degree of velocity change between the vehicles in the formation. The calculation formula is as follows:

[0099]

[0100] Where A i is the longitudinal acceleration of the i-th vehicle in the truck platoon; 1<i≤n, n is the number of vehicles in the platoon.

[0101] (3) Inter-vehicle distance: The distance between adjacent vehicles in a platoon, often used to describe the relative position of vehicles in the platoon. The calculation formula is as follows:

[0102] g i =x i+1 -x i -l

[0103] Where g i is the distance between the i-th vehicle and the vehicle in front; x i is the position of the i-th vehicle; l is the length of the vehicle in front of the i-th vehicle.

[0104] The calculation formulas for the secondary indicators and quantitative data in the security dimension are as follows:

[0105] (1) Time to Collision Reciprocal: The reciprocal of the time to collision (TTC) between adjacent vehicles in a formation, often used to assess the risk of collision between vehicles. The calculation formula is as follows:

[0106] ITTC=(v f -v r ) / (Sl)

[0107] Where, v f is the speed of the preceding vehicle; v r is the speed of the rear vehicle; S is the distance between the front and rear vehicles.

[0108] (2) Headway: The time difference between the headways of the preceding vehicles in a platoon. The calculation formula is as follows:

[0109]

[0110] Where S is the distance between the front and rear vehicles; v r is the speed of the following vehicle.

[0111] (3) Lane-changing area: The space required for platoon vehicles to change lanes on the road.

[0112] Among them, the secondary indicators in the ecological dimension include fuel consumption (FC) saving rate, CO emission saving rate, CO2 emission saving rate, NO x Emissions savings rate. Defined as the savings achieved by a platoon of vehicles of the same size compared to a non-platooning situation with the same mileage, usually expressed in %.

[0113] Specifically, in this embodiment, firstly, combined with the sustainable development of platooning technology, a truck platooning operation characteristic index system is constructed from the three levels of stability, safety, and ecology through literature reading, such as Figure 1 As shown in Tables 1 and 2, the specific operating characteristics of different control levels at various indicator levels were analyzed to preliminarily determine the threshold range for control behavior under optimal platooning conditions. This initial result was then further analyzed and verified statistically, with the results shown in Tables 1 and 2. Ultimately, the threshold range for the pilot driver to take over in typical scenarios was determined to be 4-5 seconds, and the threshold range for lane changing was 3-4 seconds.

[0114] Table 1 Verification results

[0115]

[0116] Table 2 Verification results 2

[0117]

[0118] Furthermore, based on the manipulation behavior threshold, a desktop video experiment was conducted to obtain the original experimental data;

[0119] In this embodiment, 30 or more subjects are selected to conduct a desktop video experiment and obtain raw experimental data;

[0120] The basic process of desktop experiments includes pre-experimental subjective testing, pre-experimental driving training, pre-experimental testing, and formal experiments.

[0121] Among them, the pre-experimental subjective test refers to the driving ability test based on a subjective questionnaire;

[0122] Among them, the formal experiment refers to the manipulation behavior test based on the desktop video experiment.

[0123] The pre-experiment process is the same as the formal experiment. The purpose is to familiarize yourself with the overall experimental process and basic operations. After confirmation, you can enter the formal experiment.

[0124] Specifically, this example recruited 42 subjects with a male-to-female ratio of 5:1. All subjects held Class A or Class B driver's licenses and had actual truck driving experience. The experiment was conducted in a single, closed laboratory, free of external interference. The experiment consisted of four parts: pre-experimental subjective testing, pre-experimental driver training, a preliminary experiment, and the final experiment. Raw data from each subject was obtained through the data recording module. Table 3 shows examples of some of the raw data from the subjects.

[0125] Table 3. Example of raw data

[0126]

[0127] The data in Table 3 shows examples of drivers' driving ability, obtained through a subjective questionnaire, and their control behavior, obtained through a desktop video experiment. Driving ability is measured in points, with higher scores indicating higher driving ability. Control level is measured in seconds, with shorter times indicating stronger control ability.

[0128] Furthermore, based on the original experimental data, a relationship model between the driver's driving ability and control behavior is constructed;

[0129] A driver's driving ability mainly includes three aspects: theoretical knowledge of truck platooning, practical skills of truck platooning and psychological state of driver's life.

[0130] The theoretical knowledge of truck platooning includes but is not limited to road traffic laws and regulations, autonomous driving technology, truck platooning management, emergency response, and safe driving.

[0131] Among them, truck platooning practical skills include but are not limited to basic driving operations, autonomous driving system control, truck platooning system control, communication system control, manual takeover, etc.

[0132] Among them, the psychological state of driving life includes but is not limited to strong physical fitness, flexible reaction ability, high attention and concentration, coordination and teamwork ability, etc.

[0133] Specifically, we first conducted a correlation analysis between the variables, as shown in Table 4. We then selected and established prediction models linking driving ability with takeover and lane-changing behavior. The model results were then validated, as shown in Tables 5 and 6. These results demonstrate that the established prediction models have a good fit and no collinearity issues exist between the variables. Finally, we analyzed the model accuracy, calculating the relative error and finding that the average relative errors of the two models were 6.34% and 6.89%, respectively, demonstrating good prediction accuracy.

[0134] Table 4 Correlation analysis

[0135]

[0136] Table 5 Model fitting effect analysis

[0137]

[0138] Table 6 Regression coefficients and significance tests

[0139]

[0140] Based on the above test results, the multivariate linear regression model that can express the relationship between driving ability and control behavior is determined as follows:

[0141] y1=11.579-0.039x1-0.051x2-0.03x3

[0142] y2=14.631-0.035x1-0.068x2-0.026x3

[0143] Where y1 is the prediction model between driving ability and takeover behavior; y2 is the prediction model between driving ability and lane-changing behavior.

[0144] Furthermore, the relationship model is reversely solved based on the control behavior threshold to obtain the quantitative evaluation results of the pilot car driver's driving ability;

[0145] Specifically, in this embodiment, a solution model for the minimum driving ability requirement is constructed with the driver's control behavior level as the optimization target and the control behavior threshold range as the constraint condition, as shown below.

[0146]

[0147] In the formula, x1 is the theoretical knowledge score, x2 is the practical skills score, and x3 is the student's psychological state score.

[0148] The solution results show that in a typical scenario of spilled objects on a highway, a qualified truck platoon leader driver must have a theoretical knowledge score of no less than 59, a practical skill score of no less than 80, and a psychological state score of no less than 40. The specific evaluation process is as follows: Figure 2 shown.

[0149] This example proposes a method for evaluating the driving ability of a pilot vehicle driver in a smart connected truck platoon under typical scenarios. Guided by the platoon's operational status, it identifies thresholds for the driver's control behavior level and, based on the correlation between driving ability and control behavior, performs a reverse analysis to evaluate the pilot vehicle's driving ability. This demonstrates that the present invention can more scientifically and rationally assess the minimum required driving ability for a qualified pilot vehicle driver in a smart connected truck platoon, facilitating the selection, training, and assessment of pilot vehicle drivers in truck platoons. This method is consistent with practical applications and has considerable potential for generalization.

[0150] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for evaluating the driving ability of a pilot vehicle in a platoon of intelligent connected trucks in a typical scenario, characterized by: include: Obtain operational status data of truck platoons in typical scenarios; Constructing a formation operation characteristic index system and determining a control behavior threshold of the lead vehicle driver based on the best operation state in the operation state data; Based on the manipulation behavior threshold, a desktop video experiment is conducted to obtain raw experimental data; Based on the raw data of the experiment, a relationship model between the driver's driving ability and control behavior is constructed; The driver's driving ability includes: theoretical knowledge of truck platooning, practical skills of truck platooning and the driver's psychological state; The relationship model between the driver's driving ability and control behavior is: y1=a o +a1x1+a2x2+a3x3+ε1 y2=b o +b1x1+b2x2+b3x3+ε2 Where y1 represents the prediction model between driving ability and takeover behavior, y2 represents the prediction model between driving ability and lane-changing behavior, x1 represents the theoretical knowledge score, x2 represents the practical skills score, x3 represents the psychological state score, a1, a2, a3, b1, b2, b3 represent the coefficients in the constraint conditions, a0, b0 represent the intercept terms in the linear regression model, ε1, ε2 represent the residuals; Reversely solving the relationship model based on the control behavior threshold to obtain a quantitative evaluation result of the driving ability of the pilot vehicle driver; Reversely solving the relationship model based on the manipulation behavior threshold includes: Taking the driver's control behavior level as the optimization target and the control behavior threshold as the constraint condition, a solution model for the minimum driving ability requirement is constructed; Solving the relationship model based on the solution model; The solution model is: Objective function: Min Z = c1x1 + c2x2 + c3x3 Constraints: a0+a1x1+a2x2+a3x3+ε1≥y1 b0+b1x1+b2x2+b3x3+ε2≥y2 Capacity constraints: x1+x2+x3≤300 Non-negativity constraints: x1,x2,x3≥0 in, Z Indicates the control time. S .t indicates a constraint condition.

2. The method for evaluating the driving ability of the pilot vehicle in a platoon of intelligent connected trucks under typical scenarios according to claim 1 is characterized in that: Obtaining the operational status data of truck platoons in typical scenarios includes: Determine the pilot car driver's control behavior and the characterization indicators of control behavior in typical scenarios; Based on the control behavior and characterization indicators, truck platoon simulation is carried out to obtain the operating status data of the truck platoon.

3. The method for evaluating the driving ability of the pilot vehicle in a platoon of intelligent connected trucks under typical scenarios according to claim 2 is characterized in that: The operation status data of the truck formation includes: basic information of the truck formation, location information, operation data and energy consumption and emission data; The basic information of the truck platoon includes: simulation time and vehicle number; The position information includes: horizontal position coordinates and vertical position coordinates; The operating data includes: longitudinal speed, lateral speed, longitudinal acceleration, lateral acceleration, steering wheel angle, collision time, collision time countdown, vehicle distance, and headway; The energy consumption and emission data include: CO2 emissions, NO x Emissions, CO emissions, HC emissions, and fuel consumption.

4. The method for evaluating the driving ability of the pilot vehicle in a platoon of intelligent connected trucks under typical scenarios according to claim 1 is characterized in that: The formation operation characteristic index system includes: stability dimension, safety dimension and ecological dimension; The stability dimensions include: longitudinal speed, longitudinal acceleration, and vehicle-to-vehicle distance; The longitudinal velocity is: Where V represents the longitudinal velocity, V i represents the longitudinal velocity of the i-th vehicle in the truck platoon, and n represents the number of vehicles in the platoon; The longitudinal acceleration is: Where A represents the longitudinal acceleration, A i represents the longitudinal acceleration of the i-th vehicle in the truck platoon; The workshop distance is: g i =x i+1 -x i -l Among them, g i represents the distance between the i-th vehicle and the vehicle in front, x i represents the position of the i-th vehicle; l represents the length of the vehicle in front of the i-th vehicle; The safety dimensions include: collision time countdown, headway, and lane change area; The inverse of the collision time is: ITTC=(v f -v r ) / (Sl) Among them, v f Indicates the speed of the preceding vehicle, v r It represents the speed of the rear vehicle, S represents the distance between the front and rear vehicles, and ITTC represents the inverse time to collision. The headway time is: Among them, THW represents time headway; The lane-changing area is the space required for the platoon vehicles to change lanes on the road. The ecological dimensions include: fuel consumption saving rate, CO emission saving rate, CO2 emission saving rate, NO x Emission savings rate.

5. The method for evaluating the driving ability of the pilot vehicle in a platoon of intelligent connected trucks under typical scenarios according to claim 1 is characterized in that: The desktop video experiment includes: pre-experimental subjective testing, pre-experimental driving training, pre-experimental and formal experiments; The subjective test before the experiment was: a driving ability test based on a subjective questionnaire; The formal experiment is a manipulation behavior test based on a desktop video experiment.

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